Image evaluation method that can quantify images distorted by artifacts, computer program performing the method, and computing device

ABSTRACT

An image evaluation method, including: obtaining a test image including a first lattice pattern formed by image edges; aligning the test image using the image edges to generate an aligned image including a second lattice pattern formed by aligned image edges; generating a compressed image by compressing the aligned image; and generating a quantified result by quantifying a per-pixel difference between the compressed image and the aligned image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 to Korean PatentApplication No. 10-2021-0113481 filed on Aug. 26, 2021, and KoreanPatent Application No. 10-2022-0002228 filed on Jan. 6, 2022, in theKorean Intellectual Property Office, the disclosures of which areincorporated by reference herein in their entireties.

BACKGROUND 1. Field

The disclosure relates to a technology for optimizing parameters of animage signal processor by using image evaluation and an evaluationresult, and more particularly, to an image evaluation method, a computerprogram, and a computing device capable of quantifying distortion of animage due to at least one of noise and artifacts, and optimizing theparameters of the image signal processor by using a quantified result.

2. Description of Related Art

An image processing device includes a complementarymetal-oxide-semiconductor (CMOS) substrate on which light receivingelements (e.g., photodiodes) are formed, and a color filter array formedon the CMOS substrate.

The image processing device generates a color image by processingincomplete color image data corresponding to output signals of the lightreceiving elements that receive color signals passing through the colorfilter array.

In this case, the image processing device uses an image signal processor(ISP) for the purpose of generating a color image by processingincomplete color image data. However, when the ISP processes ahigh-frequency image (e.g., an image containing image edges whosebrightness changes sharply or an image containing complex patterns),unexpected artifacts may be generated, thereby causing the degradationof quality of the image processed by the ISP.

SUMMARY

Provided are an image evaluation method capable of extracting anartifact-free reference image from a distorted image including a latticepattern and quantifying (or express the quantify of) the distortion froma difference between the extracted reference image and the image, forthe purpose of providing a reference making it possible to determine howwell an image restored through the process of restoring an image isrestored, a computer program performing the method, and a computingdevice executing the computer program.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, an image evaluationmethod includes obtaining a test image including a first lattice patternformed by image edges; aligning the test image using the image edges togenerate an aligned image including a second lattice pattern formed byaligned image edges; generating a compressed image by compressing thealigned image; and generating a quantified result by quantifying aper-pixel difference between the compressed image and the aligned image.

In accordance with an aspect of the disclosure, a computer-readablestorage medium is configured to store instructions which, when executedby at least one processor, cause the at least one processor to: obtain atest image including a first lattice pattern formed by image edges;align the test image using the image edges to generate an aligned imageincluding a second lattice pattern formed by aligned image edges:generate a compressed image by compressing the aligned image; andgenerate a quantified result by quantifying a per-pixel differencebetween the compressed image and the aligned image.

In accordance with an aspect of the disclosure, an image evaluationmethod includes obtaining a test image including a first lattice patternformed by image edges, noise, and artifacts; aligning the test imageusing the image edges to generate an aligned image including a secondlattice pattern formed by aligned image edges; removing the noise andthe artifacts from the aligned image to generate a reference imageincluding the second lattice pattern; and generating a quantified resultby quantifying a per-pixel difference between the reference image andthe aligned image.

In accordance with an aspect of the disclosure, a computer-readablestorage medium is configured to store instructions which, when executedby at least one processor, cause the at least one processor to: obtain atest image including a first lattice pattern formed by image edges,noise, and artifacts; align the test image using the image edges togenerate an aligned image including a second lattice pattern formed byaligned image edges; remove the noise and the artifacts from the alignedimage to generate a reference image including the second latticepattern; and generate a quantified result by quantifying a per-pixeldifference between the reference image and the aligned image.

In accordance with an aspect of the disclosure, a computing deviceincludes a memory device configured to store a test image including afirst lattice pattern formed by image edges, noise, and artifacts; and aprocessor configured to evaluate the test image output from the memorydevice, wherein the processor is further configured to: align the testimage using the image edges to generate an aligned image including asecond lattice pattern formed by aligned image edges; remove the noiseand the artifacts from the aligned image to generate a reference imageincluding the second lattice pattern; and quantify a per-pixeldifference between the reference image and the aligned image to generatea quantified result.

In accordance with an aspect of the disclosure, a computing deviceincludes a memory device configured to store a test image including afirst lattice pattern formed by image edges, wherein the test image isgenerated by an image signal processor based on first parameters; and atleast one processor configured to: align the image edges to obtainaligned image edges; generate an aligned image including a secondlattice pattern formed by the aligned image edges; generate a referenceimage by compressing the aligned image; generate second parameters basedon a difference between the reference image and the aligned image.

BRIEF DESCRIPTION OF THE FIGURES

The above and other aspects, features, and advantages of certainembodiments of the present disclosure will be more apparent from thefollowing description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a block diagram of a computing device executing a computerprogram capable of performing an image evaluation method according to anembodiment.

FIG. 2 is a flowchart describing an image evaluation method performed byan image evaluation computer program running on a computing deviceillustrated in FIG. 1 , according to an embodiment.

FIG. 3 is a conceptual diagram describing an image evaluation methodaccording to an embodiment.

FIG. 4 is a diagram describing a method for generating an aligned imageaccording to an embodiment.

FIG. 5A and FIG. 5B are conceptual diagrams illustrating a method forquantifying a difference between a compressed image and an aligned imagefor each pixel, according to an embodiment.

FIG. 6 is a flowchart describing an image evaluation method and a methodfor tuning parameters of an image processing processor by using theimage evaluation method, according to an embodiment.

FIG. 7 is a block diagram of an image evaluation system in whichparameters corresponding to a quantified result and raw data areexchanged over an Internet, according to an embodiment.

FIG. 8 is a block diagram of an image evaluation system in which rawdata and a quantified result are exchanged over an Internet, accordingto an embodiment.

FIG. 9 is a block diagram of an image evaluation system in which a testimage generated by an image processing processor and a quantified resultare exchanged over an Internet, according to an embodiment.

FIG. 10 is a block diagram of an image evaluation system in which a testimage stored in a memory device and a quantified result are exchangedover an Internet, according to an embodiment.

DETAILED DESCRIPTION

As is traditional in the field, the embodiments are described, andillustrated in the drawings, in terms of functional blocks, units and/ormodules. Those skilled in the art will appreciate that these blocks,units and/or modules are physically implemented by electronic (oroptical) circuits such as logic circuits, discrete components,microprocessors, hard-wired circuits, memory elements, wiringconnections, and the like, which may be formed using semiconductor-basedfabrication techniques or other manufacturing technologies. In the caseof the blocks, units and/or modules being implemented by microprocessorsor similar, they may be programmed using software (e.g., microcode) toperform various functions discussed herein and may optionally be drivenby firmware and/or software. Alternatively, each block, unit and/ormodule may be implemented by dedicated hardware, or as a combination ofdedicated hardware to perform some functions and a processor (e.g., oneor more programmed microprocessors and associated circuitry) to performother functions. Also, each block, unit and/or module of the embodimentsmay be physically separated into two or more interacting and discreteblocks, units and/or modules without departing from the present scope.Further, the blocks, units and/or modules of the embodiments may bephysically combined into more complex blocks, units and/or moduleswithout departing from the present scope.

FIG. 1 is a block diagram of a computing device executing a computerprogram capable of performing an image evaluation method according to anembodiment of the present disclosure. Referring to FIG. 1 , a computingdevice 100 includes an image sensor 110, an image signal processor (ISP)120, a memory device 130, a processor 140, and a display device 160. Inembodiments, one or more of the elements of the computing device 100 maybe for example included in, or referred to as, a camera module.

The computing device 100 may be a personal computer (PC) or a mobiledevice, and the mobile device may be a smartphone, a laptop computer, amobile Internet device (MID), a wearable computing device, a web server,or an image processing device including ISP 120.

The processor 140 executes an image evaluation computer program 150. Theprocessor 140 may be a central processing unit (CPU) or an applicationprocessor. The image evaluation computer program 150 performs operationsto be described with reference to FIGS. 2 to 5 for the purpose ofevaluating or quantifying the performance of the ISP 120.

According to embodiments, the processor 140 may further execute an ISPtuning computer program 155. The ISP tuning computer program 155performs a function of tuning the ISP 120 by using first parameters PMT,which may be for example pre-tuning parameters, a function of generatingsecond parameters PMT2 by using a quantified result value IDV generatedby the image evaluation computer program 150, and a function of tuningthe ISP 120 again by using the second parameters PMT2. The firstparameters PMT may be changed to or updated with the second parametersPMT2 by the processor 140.

Parameters of the ISP 120 may be optimized by the image evaluationcomputer program 150 and the ISP tuning computer program 155.

According to embodiments, the ISP tuning computer program 155 may set orprogram the first parameters PMT or the second parameters PMT2 to (orin) the ISP 120 by using setting values STV input from the outside orthe quantified result value IDV generated by the image evaluationcomputer program 150.

The first parameters PMT may include at least two parameters of thefollowing: a parameter for Bayer transformation, a parameter fordemosaicing, a parameter for noise reduction, a parameter for imagesharpening, a parameter for image blurting, a parameter for dead pixelcorrection, a parameter for black level compensation, a parameter forlens shading correction, a parameter for anti-aliasing noise filtering,a parameter for auto white balance (AWB) gain control, a parameter forgamma correction, a parameter for edge enhancement, a parameter forfalse color suppression, a parameter for hue/saturation control, and aparameter for brightness and contrast control.

Also, the second parameters PMT2 may include at least two of theparameters listed above.

The ISP 120 may process raw data RDATA or RDATA2 by using theabove-listed functions corresponding to the parameters PMT or PMT2 andmay generate an image TIM or TIM2 corresponding to a result of theprocessing.

The image evaluation computer program 150 may be recorded on or storedin a recording medium or storage medium readable by a computing deviceand may be connected with hardware (e.g., the processor 140) to evaluatethe performance of the ISP 120 or quantify or express the quantity ofdistortion included in an image distorted by artifacts. In embodiments,the recording medium may be a non-transitory recording medium,

The ISP tuning computer program 155 is recorded on a recording mediumreadable by the computing device and is connected with the hardware totune, set or optimize the parameters of the ISP 120. In embodiments therecording medium may be a non-transitory recording medium.

An example in which the image evaluation computer program 150 and theISP tuning computer program 155 are independent computer programs isillustrated in FIG. 1 , but the image evaluation computer program 150performing an image evaluation function or a function of quantifyingimage distortion, and the ISP tuning computer program 155 performing afunction of tuning the parameters of the ISP 120, may he implemented byone computer program.

The image sensor 110 generates the raw data RDATA or RDATA2 associatedwith an object, which may be for example a subject for photography. Theimage sensor 110 may be implemented with a complementary metal-oxidesemiconductor (CMOS) image sensor.

Before tuning, the ISP 120 converts the first raw data RDATA output fromthe image sensor 110 into the test image TIM by using the firstparameters PMT.

After the second parameters PMT2 corresponding to the quantified resultvalue IDV are set in the ISP 120, the image sensor 110 generates thesecond raw data RDATA2 associated with the object. The ISP 120 convertsthe second raw data RDATA2 output from the image sensor 110 into asecond test image TIM2 by using the second parameters PMT2.

The ISP 120 may process the raw data RDATA or RDATA2 by using thefunctions of the parameters PMT or PMT2, and may generate the test imageTIM or TIM2 depending on a result of the processing. For example, theraw data RDATA or RDATA2 output from the image sensor 110 may be datahaving a Bayer format, and the test image TIM or TIM2 may be an imagehaving an RGB format corresponding to an RGB color model, or an imagehaving a YUV format corresponding to a YUV color model.

According to embodiments, the test image TIM or TIM2 may be stored inthe memory device 130 and may then be transferred to the processor 140.In embodiments, the test image TIM or TIM2 may be directly transferredfrom the ISP 120 to the processor 140.

The display device 160 may display at least one of the test image TIM, adifference image DIFF, and the quantified result value IDV under controlof the processor 140 or the image evaluation computer program 150. Thequantified result value IDV may be a scalar value.

The display device 160 may be a light emitting diode (LED) displaydevice, an organic light emitting diode (OLED) display device, or anactive matrix OLED display device (AMOLED) display device.

FIG. 2 is a flowchart describing an image evaluation method performed byan image evaluation computer program running on a computing deviceillustrated in FIG. 1 , and FIG. 3 is a conceptual diagram describing animage evaluation method according to an embodiment of the presentdisclosure.

Before tuning, the ISP 120 converts the first raw data RDATA output fromthe image sensor 110 into the test image TIM by using the firstparameters PMT.

Referring to FIGS. 1 and 2 , and section (a) of FIG. 3 , in operationS110, the image evaluation computer program 150 receives the est imageTIM, which may be for example a chessboard image, from the memory device130 or the ISP 120. As shown in section (a) of FIG. 3 , the test imageTIM may include a first lattice pattern LP1, in which first lattices LT1formed by image edges are included. Referring to FIGS. 1 and 2 , andsection (b) of FIG. 3 , image evaluation computer program 150 accuratelyaligns a portion or the whole of the test image TIM by using the imageedges of the first lattice pattern LP1 included in the test image TIM,and generates an aligned image AIM including a second lattice patternLP2 including second lattices LT2 depending on a result of thealignment.

In embodiments, an image edge, which may be referred to as a boundaryline, may refer to a point or a boundary line at which image brightnessof a digital image sharply changes, or a point or a boundary line havingdiscontinuities. An oval EDG included in FIG. 3 illustrates exampleimage edges EG included in each of images TIM, AIM, and CIM.

According to embodiments, the first lattice pattern LP1 may refer to alattice pattern before alignment, and the second lattice pattern LP2 mayrefer to a lattice pattern after alignment.

The image evaluation computer program 150 aligns the image edges (e.g.,TXEG and TYEG as shown in section (a) of FIG. 4 ) included in the firstlattice pattern. LPI skewed on the test image TIM of section (a) of FIG.3 such that the image edges are horizontal and vertical to each other asillustrated in section (b) of FIG. 3 , and generates the aligned imageAIM including the second lattice pattern LP2 in which the secondlattices LT2 formed by aligned image edges XEG and YEG are included. Thealigned image AIM may correspond to a portion or the whole of the testimage TIM.

When viewed in an X-axis direction, which may be a horizontal direction,and a Y-axis direction, which may be a vertical direction, because thefirst lattice pattern LP1 included in the test image TIM illustrated insection (a) of FIG. 3 and section (a) of FIG. 4 is skewed, asillustrated in section (a) of FIG. 3 , the first lattices LT1 may bedifferent in size and shape.

For example, when the image sensor 110 photographing the object (e.g., achessboard) including the first lattice pattern LP1 is not parallel tothe object, the first lattice pattern LP1 of the test image TIMgenerated by the image sensor 110 may be skewed.

However, when viewed in the X-axis direction (or horizontal direction)and the Y-axis direction (or vertical direction), because the secondlattice pattern LP2 of the aligned image AIM illustrated in section (b)of FIG. 3 is not skewed, the second lattices LT2 illustrated in section(b) of FIG. 3 are identical in size and shape. The second latticepattern LP2 is a lattice pattern including image edges detected from thefirst lattice pattern LP1 through section (b) of FIG. 4 to section (i)of FIG. 4 .

As illustrated in section (b) of FIG. 3 , the second lattice pattern LP2may include a plurality of second lattices LT2, the first image edgesXEG arranged in the X-axis direction, and the second image edges YEGarranged in the Y-axis direction, and the first image edges XEG and thesecond image edges YEG are perpendicular to each other.

For example, the second lattice LT2 formed by two corresponding imageedges of the first image edges XEG and two corresponding edges of thesecond image edges YEG is in the shape of a quadrangle. In embodiments,a lattice may be referred to as a cell, a mesh, or a grid.

The image evaluation computer program 150 may accurately align the testimage TIM including the first lattice pattern LP1 by using Radontransform and may generate the aligned image AIM including the secondlattice pattern LP2 formed as a result of the alignment.

The image evaluation computer program 150 may extract a quadrilateralregion of interest ROI from the test image TIM, may warp the extractedquadrilateral ROI, and may generate the aligned image AIM including thesecond lattice pattern LP2 depending on a result of the warping. Thewarping is may be referred to as image warping.

FIG. 4 is a diagram describing a method for generating an aligned imageaccording to an embodiment of the present disclosure.

The alignment may refer to a process of generating the aligned image AIMillustrated in section (i) of FIG. 4 from the test image TIM illustratedin section (a) of FIG. 4 , and warping may refer to two processesincluding a process of obtaining four points PT1, PT2, PT3, and PT4illustrated in section (h) of FIG. 4 and a process of generating thealigned image AIM illustrated in section (i) of FIG. 4 .

Referring to FIGS. 1 to 4 , the image evaluation computer program 150detects the X-axis direction image edges TXEG and Y-axis direction imageedges TYEG from the test image TIM including the first lattice patternLP1 illustrated in section (a) of FIG. 4 by using at least one edgedetection algorithm, which may be at least one of a plurality of edgedetection algorithms. For example, the image evaluation computer program150 detects image edges by performing edge filtering in the X-axisdirection and edge filtering in the Y-axis direction on the test imageTIM.

The edge filtering or the edge detection algorithm detects the imageedges by determining whether the brightness and/or the intensity of animage changes sharply or dramatically.

Examples of edge detection algorithms include a Canny edge detector, aSobel operator, a Laplace operator, a Prewitt operator, a Scharroperator, and the like, but are not limited thereto.

For example, the image evaluation computer program 150 may detect X-axisdirection image edges and Y-axis direction image edges of each of thefirst lattices LT1 included in the first lattice pattern LP1 by applyingthe Sobel operator in each of the X-axis direction and the Y-axisdirection. The Sobel operator is may be referred to as a Sobel-Feldmanoperator or a

Sobel filter.

According to embodiments, section (b) of FIG. 4 illustrates an exampleimage EFX including image edges detected in the X-axis direction withrespect to the test image TIM by using an edge detection algorithm(e.g., the Sobel operator), and section (e) of FIG. 4 illustrates anexample image EFY including image edges detected in the Y-axis directionwith respect to the test image TIM by using the edge detectionalgorithm.

According to embodiments, section (c) of FIG. 4 illustrates an exampleX-axis direction sinogram XSI, and section (f) illustrates an exampleY-axis direction sinogram YSI.

In embodiments, the image evaluation computer program 150 may create orcalculate the X-axis direction sinogram XSI, which may correspond to theimage EFX edge-filtered in the X-axis direction (as shown in section (b)of FIG. 4 ), and may create or calculate the Y-axis direction sinogramYSI, which may correspond to the image EFY edge-filtered in the Y-axisdirection (as shown in section (e) of FIG. 4 ).

For example, the image evaluation computer program 150 that uses theRadon transform creates the X-axis direction sinogram XSI illustrated insection (c) of FIG. 4 from the image EFX edge-filtered in the X-axisdirection and creates the Y-axis direction sinogram YSI illustrated insection (f) of FIG. 4 from the image EFY edge-filtered in the Y-axisdirection.

The image evaluation computer program 150 filters the X-axis directionsinogram XSI to generate a first filtered sinogram FSX illustrated insection (d) of FIG. 4 and filters the Y-axis direction sinogram YSI togenerate a second filtered sinogram FSY illustrated in section (g) ofFIG. 4 .

For example, the image evaluation computer program 150 creates the firstfiltered sinogram FSX by extracting points, each of which has a highintensity, from among points arranged in each row of the X-axisdirection sinogram XSI and creates the second filtered sinogram FSY byextracting points, each of which has a high intensity, from among pointsarranged in each row of the Y-axis direction sinogram YSI. Inembodiments, a high intensity may refer to, for example, a highestintensity from among intensities, or a relatively high intensity incomparison with other intensities.

Section (h) of FIG. 4 illustrates an image DEIM including image edgesdetected from the test image TIM by using an edge detection algorithm.The image evaluation computer program 150 generates the image DEIMincluding the image edges detected from the test image TIM by using thefirst filtered sinogram FSX illustrated in section (d) of FIG. 4 and thesecond filtered sinogram FSY illustrated in section (g) of FIG. 4 .

The image evaluation computer program 150 generates the image DEIMincluding the four points PT1, PT2, PT3, and PT4 by using two points PX1and PX2 of points included in the first filtered sinogram FSX and twopoints PY1 and PY2 included in the second filtered sinogram FSY.

For example, the image evaluation computer program 150 determines thefirst point PT1 by using the two points PX1 and PY1, determines thesecond point PT2 by using the two points PX1 and PY2, determines thethird point PT3 by using the two points PX2 and PY2, and determines thefourth point PT4 by using the two points PX2 and PY1.

The image evaluation computer program 150 warps the image DEIM includingthe detected image edges by using the four points PT1, PT2, PT3, and PT4and generates the aligned image AIM including the four points PT1, PT2,PT3, and PT4 depending on a result of the warping.

For example, referring to section (b) of FIG. 3 , to generate thealigned image AIM including the second lattice pattern LP2 composed of8×8 lattices LT2, the image evaluation computer program 150 may selectthe two first points PX1 and PX2 from a first group of points of thefirst filtered sinogram FSX, may select the two second points PY1 andPY2 from a second group of points of the second filtered sinogram FSY,and may generate the image DEIM including the four points PT1, PT2, PT3,and PT4 selected.

For example, each point included in the first filtered sinogram FSXillustrated in section (d) of FIG. 4 and each point included in thesecond filtered sinogram FSY illustrated in section (g) of FIG. 4 maycorrespond to image edges in the actual image DEIM or AIM, respectively.

For example, when the first point PX1 is the lowest point of a firstgroup of points, the first point PX2 is the highest point of the firstgroup of points, the second point PYI is the lowest point of a secondgroup of points, the second point PY2 is the highest point of the secondgroup of points, then the image DEM including the four points PT1, PT2,PT3, and PT4 has the largest size compared to images each including fourdifferent points.

A size of a quadrilateral ROI targeted for warping is determineddepending on whether the image evaluation computer program 150 selectsany two first points from a first group of points and selects any twosecond points from a second group of points.

For example, based on the image evaluation computer program 150selecting two first points from the remaining points other than thelowest point and the highest point of a first group of points, andselecting two second points from the remaining points other than thelowest point and the highest point of a second group of points, theimage evaluation computer program 150 extracts a quadrilateral ROIcorresponding to the four points thus selected, and warps the extractedquadrilateral ROI to generate the aligned image AIM.

Returning to FIG. 3 , in operation S120, the image evaluation computerprogram 150 compresses the image AIM aligned through operation S110 byusing truncated singular value decomposition and generates thecompressed image CIM including the second lattice pattern LP2 formed bythe second lattices LT2 illustrated in section (c) of FIG. 3 .

Each of the test image TIM and the aligned image AIM may be an imagedistorted by noise and artifacts, but the compressed image CIM may be animage which does not include the noise and artifacts, that is, adistortion-free image.

Image distortion includes distortion in which a shape (e.g., a latticepattern) changes depending on an image viewing angle, and distortion inwhich an outline, a contour, or a boundary line of an image edge or atwo-dimensional point (or an apex in a three-dimensional image) isunclear due to imperfect performance of the ISP 120.

In operation S130, the image evaluation computer program 150 quantifiesa difference between the compressed image CIM illustrated in section (c)of FIG. 3 and the aligned image AIM illustrated in section (b) of FIG. 3in units of a pixel and generates the difference image DIFF and thequantified result value IDV depending on a quantified result. Inembodiments, quantifying a difference in units of a pixel may meandetermining or calculating a difference for each pixel, or for exampledetermining or calculating a per-pixel difference.

FIGS. 5A and 5B are a conceptual diagrams illustrating a method forquantifying a difference between a compressed image and an aligned imagefor each pixel.

In embodiments, an aligned image AIMa illustrated in FIG. 5A may be animage including 4×4 pixels and a compressed image CIMa illustrated inFIG. 5B may be an image including 4×4 pixels. The aligned image AIMaincluding the 4×4 pixels may correspond to a portion of the alignedimage AIM illustrated in section (b) of FIG. 3 , and the compressedimage CIMa including the 4×4 pixels may correspond to a portion of thecompressed image CIM illustrated in section (c) of FIG. 3 .

The image evaluation computer program 150 calculates a differencebetween pixel values of pixels targeted for comparison in units of apixel.

A pixel is the smallest element of an image. Each pixel corresponds toone pixel value.

For example, in an 8-bit gray scale image, a pixel value (i.e., a valueof a pixel) ranges in value from 0 to 255. A pixel value at each pointcorresponds to the intensity of light photons striking each point. Eachpixel has a value that is proportional to a light intensity measured orreceived at a corresponding location.

In embodiments, a pixel value is not limited to the above definition,and may be defined in a different manner. However, for convenience ofdescription, each of the images CIM, CIMa, AIM, and AIMa may bedescribed herein as an 8-bit gray scale image.

As illustrated in FIGS. 5A and 5B as an example, the image evaluationcomputer program 150 calculates differences between pixel values of the16 pixels included in the compressed image CIM and pixel values of the16 pixels included in the aligned image AIMa in a pixelwise manner, andthus, 16 difference values are generated.

For example, the image evaluation computer program 150 calculates adifference between a pixel value VP2_11 of a pixel P2_11 and a pixelvalue VP1_11 of a pixel P1_11, a difference between a pixel value VP2_14of a pixel P2_14 and a pixel value VP1_14 of a pixel P1_14, a differencebetween a pixel value VP2_41 of a pixel P2_41 and a pixel value VP1_41of a pixel P1_41, and a difference between a pixel value VP2_44 of apixel P2_44 and a pixel value VP1_44 of a pixel P1_44.

For convenience of description, the method for calculating 4 differencesis described above as an example, but a method for calculating 12differences, which is not described, may be sufficiently understood fromthe method for calculating 4 differences.

The image evaluation computer program 150 calculates the differencesbetween pixel values of pixels included in the compressed image CIM andpixel values of pixels included in the aligned image AIM in units of apixel and generates the difference image DIFF by using the differences.

Referring to section (d) of FIG. 3 , the image evaluation computerprogram 150 generates the difference image DIFF including thedifferences calculated in units of a pixel.

The difference image DIFF includes bright points BR and dark points DK.

Each of the bright points BR indicates that a calculated differencebetween corresponding pixel values is relatively large, and each of thedark points DK indicates that a calculated difference betweencorresponding pixel values is relatively small.

The difference image MIT illustrated in section (d) of FIG. 3 isexpressed by using a black and white image, but in embodiments the imageevaluation computer program 150 may express the difference image DIFF byusing a color image.

When the image evaluation computer program 150 expresses the differenceimage DIFF by using a color image, the image evaluation computer program150 may increase brightness of a corresponding point as a differencebetween two corresponding pixel values increases and may decreasebrightness of a corresponding point as a difference between twocorresponding pixel values decreases.

For example, as the brightness of a bright point BR becomes brighter,the degree of image distortion corresponding to the bright point BRbecomes larger; as the brightness of a dark point DK becomes darker, thedegree of image distortion corresponding to the dark point DK becomessmaller.

The image evaluation computer program 150 calculates the differencesbetween the pixel values of the pixels included in the compressed imageCIM and the pixel values of the pixels included in the aligned image AIMin units of a pixel and generates the quantified result value IDV byusing the differences.

Referring to section (e) of FIG. 3 , the image evaluation computerprogram 150 generates the quantified result value IDV, which may bereferred to as an image distance, by using the differences calculated inunits of a pixel.

For example, the image evaluation computer program 150 generates thequantified result value IDV by quantifying the differences between thepixel values of the pixels included in the compressed image CIM and thepixel values of the pixels included in the aligned. image AIM in unitsof a pixel by using the L1 norm, L2 norm, or structural similarity indexmeasure (SSIM).

The L1 norm may also be referred to as the Manhattan distance or Taxicabgeometry, and the L2 norm may also be referred to as the Euclideandistance.

That is, the image evaluation computer program 150 may generate thequantified result value IDV having a scalar value by using the L1 norm,L2 norm, or SSIM.

In operation S130, the image evaluation computer program 150 maygenerate at least one of the difference image DIFF and the quantifiedresult value IDV based on a difference between two corresponding pixelvalues, as described with reference to FIGS. 5A and 5B.

The image evaluation computer program 150 may send at least one of thetest image TIM, the difference image DIFF, and the quantified resultvalue IDV to the display device 160, and the display device 160 maydisplay at least one of the test image TIM, the difference image DIFF,and the quantified result value IDV. Accordingly, the user of thecomputing device 100 may visually see at least one of the test imageTIM, the difference image D1FF, and the quantified result value IDVthrough the display device 160.

The ISP tuning computer program 155 may receive the quantified resultvalue IDV generated by the image evaluation computer program 150, maygenerate the second. parameters PMT2 by using the quantified resultvalue IDV, and may tune the 1SP 120 by using the second parameters PMT2.Accordingly, the first parameters PMT set in the ISP 120 are updatedwith the second parameters PMT2.

After the second parameters PMT2 are set in the ISP 120 (or areprogrammed or updated in the ISP 120), the image sensor 110 photographsan object to generate the second raw data RDATA2.

The ISP 120 receives the second raw data RDATA2, processes the secondraw data RDATA2 by using the second parameters PMT2, and generates thesecond test image TIM2 corresponding to a processing result. The secondtest image T1M2 may be sent to the memory device 130 or the imageevaluation computer program 150.

The image evaluation computer program 150 generates a difference imageand/or a quantified result value from the second test image TIM2 usingthe method described with reference to FIGS. 1 to 5B. The ISP tuningcomputer program 155 may generate third parameters by using thequantified result value and may update the second parameters PMT2 set inthe ISP 120 with the third parameters.

For example, the image evaluation computer program 150 may generate thealigned image AIM including the second lattice pattern LP2 formed by thealigned image edges XEG and YEG by aligning the test image TIM includingthe first lattice pattern LP1 including the first lattices LT1 formed bythe image edges TXEG and TYEG, noise, and artifacts in operation S110,may generate the reference image, which may correspond to the compressedimage CLM, including only the second lattice pattern LP2 by removing thenoise and artifacts among the noise, the artifacts, and the secondlattice pattern LP2 included in the aligned image AIM in operation S120,and may generate a quantified result (e.g., the difference image DIFFand/or the quantified result value IDV) by quantifying differences ofthe reference image and the aligned image AIM in units of a pixel inoperation S130.

In other words, the image evaluation computer program 150 may generatethe aligned image AIM by using one source image TIM, may generate thecompressed image by compressing the aligned image AIM, and may generatea quantified result (e.g., the difference image DIFF and/or thequantified result value IDV) by quantifying differences of the referenceimage and the aligned image AIM in units of a pixel by using thecompressed image CLM as a reference image from which the imagedistortion is absent. Accordingly, the image evaluation computer program150 may quantify the degree of image distortion of the test image TIMand may generate a quantified result (e.g., the difference image DIFFand/or the quantified result value IDV).

FIG. 6 is a flowchart describing an image evaluation method and a methodfor tuning parameters of an image processing processor by using theimage evaluation method, according to an embodiment of the presentdisclosure.

Referring to FIGS. 1 to 6 , in operation S210, the image evaluationcomputer program 150 tunes the ISP 120 by using the first parameters PMTbefore testing the performance of the ISP 120.

In operation S220, the ISP 120 receives the first raw data RDATA fromthe image sensor 110 and converts the first raw data RDATA into the testimage TIM by using the first parameters PMT.

In operation S230, the image evaluation computer program 150 quantifiesa difference between the compressed image CIM and the aligned image AIMin units of a pixel and may generate a quantified result (e.g., thedifference image DIFF and/or the quantified result value IDV).

According to embodiments, the user of the computing device 100 mayvisually check the difference image DIFF and/or the quantified resultvalue IDV displayed through the display device 160 and may decidewhether to perform additional ISP tuning.

When the user of the computing device 100 inputs setting values STVindicating additional tuning for the ISP 120 to the ISP tuning computerprogram 155 by using an input device (e.g., a keyboard or a touchscreen) (Yes in operation S240), in operation S210, the ISP tuningcomputer program 155 may generate the second parameters PMT2corresponding to the setting values STV and may tune the ISP 120 byusing the second parameters PMT2.

When the user of the computing device 100 inputs the setting values STV,which indicate that the additional tuning for the ISP 120 is notrequired, to the ISP tuning computer program 155 by using the inputdevice (No in operation S240), the ISP tuning computer program 155 mayterminate the method in response to the setting values STV.

According to embodiments, the ISP tuning computer program 155 maycompare the quantified result value DV output from the image evaluationcomputer program 150 with a reference value in operation S240; dependingon a comparison result (Yes in operation S240 or No in operation S240),the ISP tuning computer program 155 may continue to perform the tuningon the ISP 120 in operation S210 or may terminate the method.

According to embodiments, the ISP tuning computer program 155 may beprogrammed to generate the second parameters PMT2 corresponding to thequantified result value IDV when the quantified result value IDV isgreater than the reference value.

However, according to embodiments, the ISP tuning computer program 155may be programmed to generate the second parameters PMT2 correspondingto the quantified result value IDV when the quantified result value IDVis smaller than the reference value.

In operation S240, the ISP tuning computer program 155 determineswhether to perform additional tuning on the ISP 120 by using the settingvalues STV input by the user or the quantified result value IDV outputfrom the image evaluation computer program 150.

When the additional tuning is required (Yes in operation S240), inoperation S210, the ISP tuning computer program 155 generates the secondparameters PMT2 by using the setting values STV or the quantified resultvalue IDV and tunes the ISP 120 by using the second parameters PMT2.

However, when the additional tuning is not required (No in operationS240), the ISP tuning computer program 155 may terminate the method.

After the ISP 120 is tuned based on the second parameters PMT2, inoperation S220, the ISP 120 receives the second raw data RDATA2 from theimage sensor 110 and converts the second raw data RDATA2 into the secondtest image TIM2 by using the second raw data RDATA2.

In operation S230, the image evaluation computer program 150 generatesan aligned image from the second test image TIM2 as described withreference to (b) of FIG. 3 , generates a compressed image from thealigned image as described with reference to (c) of FIG. 3 , quantifiesa difference between the compressed image and the aligned image in unitsof a pixel as described with reference to section (d) and section (e) ofFIG. 3 , and generates a difference image and a quantified result valuecorresponding to a quantifying result.

In operation S240, the image evaluation computer program 150 determineswhether to perform additional tuning for the ISP 120, for example bydetermining whether the additional tuning is required or otherwisedesired. For example, in embodiments the image evaluation computerprogram 150 may determine whether to perform additional tuning bycomparing a difference corresponding to the difference image to athreshold difference, or by comparing the quantified result value to athreshold value. When the image evaluation computer program 150determines to perform additional tuning for the ISP 120 (Yes inoperation S240), in operation S210, the ISP tuning computer program 155receives a quantified result value from the image evaluation computerprogram 150, generates third parameters by using the quantified result,value, and again tunes the ISP 120 by using the third parameters. Whenthe image evaluation computer program 150 determines not to perform theadditional tuning (No in operation S240), the ISP tuning computerprogram 155 may terminate the method.

Operation S210 to operation S240 of FIG. 6 may be repeatedly performeduntil it is determined not, to additionally tune the ISP 120, forexample until there is no need to additionally tune the ISP 120, or forexample until optimal parameters are set in the ISP 120.

FIGS. 7 to 10 are block diagrams of image evaluation systems, accordingto embodiments. In embodiments, one or more of the ISP 340, the ISP 341,and the ISP 520 may correspond to the ISP 120 discussed above. Inembodiments, the display device 350 may correspond to the display device160 discussed above. In embodiments, the image sensor 310 may correspondto the image sensor 110 discussed above. In embodiments, one or more ofthe processor 320, the processor 321, and the processor 530 maycorrespond to the processor 140 discussed above. In embodiments, thememory device 305 may correspond to the memory device 130 discussedabove discussed above.

FIG. 7 is a block diagram of an image evaluation system in whichparameters corresponding to a quantified result and raw data areexchanged over an Internet, according to an embodiment of the presentdisclosure.

Referring to FIG. 7 , an image evaluation system 200A includes acomputing device 300A and a server 500A communicating with each otherover a communication network. The server 500A may be a computing devicethat executes the image evaluation computer program 150 performing animage evaluation operation, and the ISP tuning computer program 155generating the parameters PMT2 for tuning an ISP 340 included in thecomputing device 300A.

The communication network to be described FIGS. 7 to 10 may be anInternet or a Wi-Fi network.

The raw data RDATA output from image sensor 310, which may be forexample a digital camera, are sent to a communication device 510 of theserver 500A over a communication device 330 and the communicationnetwork, under control of a processor 320.

An ISP 520 of the server 500A may be tuned by parameters, and the ISP520 converts the raw data RDATA from the computing device 300A into thetest image TIM by using the parameters.

The image evaluation computer program 150 executed by a processor 530 ofthe server 500A generates the quantified result value IDV. The ISPtuning computer program 155 generates the parameters PMT2 by using thequantified result value IDV generated by the image evaluation computerprogram 150 and sends the parameters PMT2 to the communication device330 of the computing device 300A using a communication device 510 andthe communication network. The processor 320 of the computing device300A tunes the ISP 340 of the computing device 300A by using theparameters PMT2 received through the communication device 330. The ISP340 tuned by the parameters PMT2 may convert raw data output from thedigital camera 310 into an image by using the parameters PMT2.

FIG. 8 is a block diagram of an image evaluation system in which rawdata and a quantified result are exchanged over an Internet, accordingto an embodiment of the present disclosure.

Referring to FIG. 8 , an image evaluation system 200B includes acomputing device 300B and a server 500B communicating with each otherover the communication network. The server 500B may be a computingdevice that executes the image evaluation computer program 150performing an image evaluation operation.

The raw data RDATA output from the image sensor 310, which may be forexample a digital camera, are sent to the communication device 510 ofthe server 500B over the communication device 330 and the communicationnetwork, under control of a processor 321.

The ISP 520 of the server 500B may be tuned by parameters, and the ISP520 converts the raw data RDATA from the computing device 300B into thetest image TIM by using the parameters.

The image evaluation computer program 150 executed by a processor 531 ofthe server 500B generates the quantified result value IDV. Thequantified result value IDV is sent to the communication device 330 ofthe computing device 300B over the communication device 510 and thecommunication network.

The ISP tuning computer program 155 executed by the processor 321 of thecomputing device 300B generates the parameters PMT2 by using thequantified result value IDV received through the communication device330 and tunes the ISP 340 of the computing device 300B by using theparameters PMT2. The ISP 340 tuned by the parameters PMT2 may convertraw data output from the digital camera 310 into an image by using theparameters PMT2. The quantified result value IDV may be displayed on adisplay device 350 under control of the processor 321.

FIG. 9 is a block diagram of an image evaluation system in which a testimage generated by an image processing processor and a quantified resultare exchanged over an Internet, according to an embodiment of thepresent disclosure.

Referring to FIG. 9 , an image evaluation system 200C includes acomputing device 300C and a server 500C communicating with each otherover the communication network. The server 5000 may be a computingdevice that executes the image evaluation computer program 150performing an image evaluation operation.

The raw data RDATA output from the or image sensor 310, which may he forexample a digital camera, are sent to an ISP 341. The ISP 341 tuned bythe first parameters PMT receives the raw data RDATA and converts theraw data RDATA into the test image TIM by using the first parametersPMT.

The test image TIM is sent to the communication device 510 of the server500C through the communication device 330 and the communication network,under control of the processor 321.

The image evaluation computer program 150 executed by the processor 531of the server 500C processes the test image TIM to generate thequantified result value IDV. The quantified result value IDV is sent tothe communication device 330 of the computing device 300C over thecommunication device 510 and the communication network.

The ISP tuning computer program 155 executed by the processor 321 of thecomputing device 300C generates the parameters PMT2 by using thequantified result value IDV received through the communication device330 and tunes the ISP 341 of the computing device 300C by using theparameters PMT2. The ISP 341 tuned by the second parameters PMT2 mayconvert raw data output from the digital camera 310 into an image byusing the second parameters PMT2. The quantified result value IDV may bedisplayed on the display device 350 under control of the processor 321.

FIG. 10 is a block diagram of an image evaluation system in which a testimage stored in a memory device and a quantified result are exchangedover an Internet, according to an embodiment of the present disclosure.

Referring to FIG. 10 , an image evaluation system 200D includes acomputing device 300D and a server 500D communicating with each otherover the communication network. The server 500D may be a computingdevice that executes the image evaluation computer program 150performing an image evaluation operation.

The test image TIM stored in a memory device 305 is sent to thecommunication device 510 of the server 500D over the communicationdevice 330 and the communication network, under control of the processor320.

The image evaluation computer program 150 executed by the processor 531of the server 500D processes the test image TIM and generates thequantified result value IDV and the difference image DIFF correspondingto a quantified result. At least one of the difference image DIFF andthe quantified result value IDV is sent to the communication device 330of the computing device 300D over the communication device 510 and thecommunication network.

At least one of the difference image DIFF and the quantified resultvalue IDV received through the communication device 330 may be displayedon the display device 350 under control of the processor 320 of thecomputing device 300D.

As described with reference to FIGS. 1 to 10 , the image evaluationcomputer program 150 may obtain an artifact-free reference image from analigned image by using the phenomenon that an image becomes a latticepattern, for example a chessboard pattern image, when the image iscompressed, or when the image is highly compressed or extremelycompressed, through feature value decomposition, for example truncatedsingular value decomposition (SVD), may detect artifacts, which may befor example structural artifacts, included in the image by using theartifact-free reference image, and may generate a quantified result byquantifying a detection result. Herein, the structural artifacts maymean artifacts generated by an ISP.

The image evaluation computer program 150 may generate an artifact-freereference image (e.g., a compressed image) from a single image (e.g., atest image) by using SVD image compression.

The image evaluation computer program 150 may create a reference imagein which the image sharpness, referring to the degree to which a shapeof an outline included in an image appears clearly, is invariant, andmay generate a quantified result by quantifying a difference thereference image and an image including artifacts.

The image evaluation computer program 150 may quantify structuraldistortion included in the image and may generate a quantified result.

The image evaluation computer program 150 may align a test imageincluding a lattice pattern formed by image edges more finely by usingthe Radon transform and may create an aligned image including thealigned image edges.

The image evaluation computer program 150 may quantify the detail of animage distorted by artifacts to perform quantitative evaluation on theimage.

According to an embodiment of the present disclosure, an imageevaluation method and a computer program performing the image evaluationmethod may accurately align a test image (e.g., a chessboard image)including a first lattice pattern formed by image edges by using theimage edges.

According to an embodiment of the present disclosure, an imageevaluation method and a computer program performing the image evaluationmethod may quantify, or express the quantity of, distortion appearing ina test image (e.g., chessboard image) and thus may match the degree ofdistortion, which a human may perceive with respect to the test image,and the degree of quantified distortion.

According to an embodiment of the present disclosure, an imageevaluation method and a computer program performing the image evaluationmethod may generate a test image by using parameters set in an imageprocessing processor before tuning, may accurately align the test imageto generate an aligned image, may compress the aligned image to generatea compressed image, may quantify a difference between the compressedimage and the aligned image in units of a pixel, may generate newparameters by using a quantified result, and may again tune the imageprocessing processor by using the new parameters.

Accordingly, according to an embodiment of the present disclosure, animage evaluation method and a computer program performing the imageevaluation method may optimize parameters of an image processingprocessor.

While the present disclosure has been described with reference toembodiments thereof, it will be apparent to those of ordinary skill inthe art that various changes and modifications may be made theretowithout departing from the spirit and scope of the present disclosure asset forth in the following claims.

What is claimed is:
 1. An image evaluation method comprising: obtaininga test image including a first lattice pattern formed by image edges;aligning the test image using the image edges to generate an alignedimage including a second lattice pattern formed by aligned image edges;generating a compressed image by compressing the aligned image; andgenerating a quantified result by quantifying a per-pixel differencebetween the compressed image and the aligned image.
 2. The imageevaluation method of claim 1, wherein the generating of the alignedimage comprises: aligning each image edge of the image edges in at leastone of a horizontal direction and a vertical direction; and generatingthe aligned image including the second lattice pattern formed by thealigned image edges.
 3. The image evaluation method of claim 1, whereinthe generating of the aligned image comprises aligning the test imageusing Radon transform to generate the aligned image.
 4. The imageevaluation method of claim 1, wherein the generating of the alignedimage comprises: extracting a quadrilateral region of interest from thetest image; and warping the extracted quadrilateral region of interestto generate the aligned image.
 5. The image evaluation method of claim1, wherein the generating of the compressed image comprises: compressingthe aligned image using truncated singular value decomposition togenerate the compressed image.
 6. The image evaluation method of claim1, further comprising: tuning an image signal processor using firstparameters; converting, by the image signal processor, first raw dataoutput from an image sensor into the test image based on the firstparameters; generating second parameters using the quantified result;tuning the image signal processor using the second parameters; andconverting, by the image signal processor, second raw data output fromthe image sensor into a second test image based on the secondparameters.
 7. The image evaluation method of claim 1, furthercomprising: receiving raw data output from an image sensor included in acomputing device over a communication network; converting the raw datainto the test image using a first image signal processor; generatingparameters for tuning a second image signal processor included in thecomputing device using the quantified result; and transmitting theparameters to the computing device over the communication network. 8.The image evaluation method of claim 1, further comprising: receivingraw data output from an image sensor included in a computing device overa communication network; converting the raw data into the test imageusing an image signal processor; and sending the quantified result tothe computing device over the communication network.
 9. The imageevaluation method of claim 1, further comprising: receiving the testimage from a computing device over a communication network; and sendingthe quantified result to the computing device over the communicationnetwork.
 10. A computer-readable storage medium configured to storeinstructions which, when executed by at least one processor, cause theat least one processor to: obtain a test image including a first latticepattern formed by image edges; align the test image using the imageedges to generate an aligned image including a second lattice patternformed by aligned image edges; generate a compressed image bycompressing the aligned image; and generate a quantified result byquantifying a per-pixel difference between the compressed image and thealigned image.
 11. An image evaluation method comprising: obtaining atest image including a first lattice pattern formed by image edges,noise, and artifacts; aligning the test image using the image edges togenerate an aligned image including a second lattice pattern formed byaligned image edges; removing the noise and the artifacts from thealigned image to generate a reference image including the second latticepattern; and generating a quantified result by quantifying a per-pixeldifference between the reference image and the aligned image.
 12. Theimage evaluation method of claim 11, wherein the generating of thealigned image comprises aligning each image edge of the image edges inat least one of a horizontal direction and a vertical direction, andgenerating the aligned image including the second lattice pattern formedby the aligned image edges.
 13. The image evaluation method of claim 11,wherein the generating of the reference image comprises compressing thealigned image using truncated singular value decomposition to generatethe reference image.
 14. The image evaluation method of claim 11,further comprising: tuning an image signal processor using firstparameters; converting, by the image signal processor, first raw dataoutput from an image sensor into the test image based on the firstparameters; generating second parameters using the quantified result;tuning the image signal processor using the second parameters; andconverting, by the image signal processor, second raw data output fromthe image sensor into a second test image based on the secondparameters.
 15. A computer-readable storage medium configured to storeinstructions which, when executed by at least one processor, cause theat least one processor to: obtain a test image including a first latticepattern formed by image edges, noise, and artifacts; align the testimage using the image edges to generate an aligned image including asecond lattice pattern formed by aligned image edges; remove the noiseand the artifacts from the aligned image to generate a reference imageincluding the second lattice pattern; and generate a quantified resultby quantifying a per-pixel difference between the reference image andthe aligned image.
 16. A computing device comprising: a memory deviceconfigured to store a test image including a first lattice patternformed by image edges, noise, and artifacts; and a processor configuredto evaluate the test image output from the memory device, wherein theprocessor is further configured to: align the test image using the imageedges to generate an aligned image including a second lattice patternformed by aligned image edges; remove the noise and the artifacts fromthe aligned image to generate a reference image including the secondlattice pattern; and quantify a per-pixel difference between thereference image and the aligned image to generate a quantified result.17. The computing device of claim 16, further comprising: an imagesensor configured to generate first raw data; and an image signalprocessor configured to convert the first raw data into the test imageusing first parameters, wherein the processor is further configured togenerate second parameters using the quantified result, and update thefirst parameters set in the image signal processor to the secondparameters.
 18. The computing device of claim 16, further comprising: animage sensor configured to generate first raw data and second raw data;and an image signal processor, wherein the processor is furtherconfigured to tune the image signal processor using first parameters,generate second parameters using the quantified result, and tune theimage signal processor again using the second parameters, wherein basedon the image signal processor being tuned using the first parameters,the image signal processor is configured to convert the first raw datainto the test image, and wherein based on the image signal processorbeing tuned using the second parameters, the image signal processor isconfigured to convert the second raw data into a second test image. 19.The computing device of claim 16, wherein the processor is furtherconfigured to generate the aligned image by aligning each image edge ofthe image edges in at least one of a horizontal direction and a verticaldirection,
 20. The computing device of claim 16, wherein the processoris further configured to generate the reference image by compressing thealigned image using truncated singular value decomposition.
 71. Acomputing device comprising: a memory device configured to store a testimage including a first lattice pattern formed by image edges, whereinthe test image is generated by an image signal processor based on firstparameters; and at least one processor configured to: align the imageedges to obtain aligned image edges; generate an aligned image includinga second lattice pattern formed by the aligned image edges; generate areference image by compressing the aligned image; and generate secondparameters based on a difference between the reference image and thealigned image.
 22. The computing device of claim 21, wherein the imagesignal processor is included in the computing device, and wherein the atleast one processor is further configured to tune the image signalprocessor based on the second parameters.
 23. The computing device ofclaim 21, wherein the at least one processor is further configured totransmit the second parameters to an external computing device in orderto tune an external image signal processor included in the externalcomputing device.